Build software that combines Python’s expressivity with the performance and control of C (and C++). It’s possible with Cython, the compiler and hybrid programming language used by foundational packages such as NumPy, and prominent in projects including Pandas, h5py, and scikits-learn. In this practical guide, you’ll learn how to use Cython to improve Python’s performance—up to 3000x— and to wrap C and C++ libraries in Python with ease.
- Use Cython’s static typing to speed up Python code
- Gain hands-on experience using Cython features to boost your numeric-heavy Python
- Create new types with Cython—and see how fast object-oriented programming in Python can be
- Effectively organize Cython code into separate modules and packages without sacrificing performance
- Use Cython to give Pythonic interfaces to C and C++ libraries
- Optimize code with Cython’s runtime and compile-time profiling tools
- Use Cython’s prange function to parallelize loops transparently with OpenMP
Kurt W. Smith has been using Python in scientific computing ever since his college days, looking for any opportunity to incorporate it into his computational physics classes. He has contributed to the Cython project as part of the 2009 Google Summer of Code, implementing the initial version of typed memoryviews and native cython arrays. He uses Cython extensively in his consulting work at Enthought, training hundreds of scientists, engineers, and researchers in Python, NumPy, Cython, and parallel and high-performance computing.